In the field of image processing, image matching refers to that a reference image is inputted in advance, thereafter a detected image is matched with the reference image, a pattern from the reference image is detected in the detected image, and a homography matrix necessary for coordinate transform between the detected image and the reference image is obtained. Image matching can be used in augmented reality, target detection, autonomous driving, missile-end visual precision guidance, and other application fields. At present, there are many existing feature detection algorithms that are robust to image transform and scaling and therefore can be used for image matching. However, these traditional feature detection algorithms usually have no robustness to distortion of images captured at large tilt angles, images captured at large tilt angles therefore will show deformation with a disproportionate aspect ratio, resulting in a failure of the existing feature matching algorithms.